基于自适应混合树搜索的最大似然MIMO检测

Kuei-Chiang Lai, Jiun-Jie Jia, Li-Wei Lin
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引用次数: 3

摘要

提出了一种用于空间复用系统中最大似然符号检测的混合树搜索算法。从本质上讲,搜索树以宽度优先(BF)的方式迭代扩展,直到当前最可能路径正确的概率超过指定的阈值,此时启动深度优先(DF)阶段以遍历树的其余部分。与球面译码(SD)算法相比,该算法利用BF阶段来提高初始DF搜索方向的精度,利用SM方案固有的多样性。仿真结果表明,在许多实际应用场景下,该算法的复杂度明显低于SD算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximum-likelihood MIMO detection using adaptive hybrid tree search
A hybrid tree search algorithm is described for maximum-likelihood symbol detection in spatial multiplexing (SM) systems. Essentially, the search tree is iteratively expanded in the breadth-first (BF) manner until the probability that the current most likely path is correct exceeds a specified threshold, at which point the depth-first (DF) stage is initiated to traverse the rest of the tree. In contrast to the sphere decoding (SD) algorithm, the proposed algorithm uses the BF stage to enhance the accuracy of the initial DF search direction, by exploiting the diversity inherent in the SM scheme. Simulation results demonstrate that the proposed algorithm achieves a significantly lower complexity than the SD algorithm in many scenarios of practical interest.
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